
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly.graph_objs as go
import plotly.offline as py
import seaborn as sns
import matplotlib.ticker as mtick
plt.style.use('fivethirtyeight')
import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
import os


#checking current Directory

os.getcwd()

'C:\\Users\\tusha'


#Changing the directory

os.chdir("C:\\Users\\tusha\\Desktop\\Zomato Rating Project\\")
os.getcwd()

'C:\\Users\\tusha\\Desktop\\Zomato Rating Project'


#Reading data

data = pd.read_csv('zomato.csv')
display(data)


display(data.shape)

(51717, 17)


#checking the data types
print (data.dtypes )

url                            object
address                        object
name                           object
online_order                   object
book_table                     object
rate                           object
votes                           int64
phone                          object
location                       object
rest_type                      object
dish_liked                     object
cuisines                       object
approx_cost(for two people)    object
reviews_list                   object
menu_item                      object
listed_in(type)                object
listed_in(city)                object
dtype: object


#Checking null values
print (data.isna().sum() )

url                                0
address                            0
name                               0
online_order                       0
book_table                         0
rate                            7775
votes                              0
phone                           1208
location                          21
rest_type                        227
dish_liked                     28078
cuisines                          45
approx_cost(for two people)      346
reviews_list                       0
menu_item                          0
listed_in(type)                    0
listed_in(city)                    0
dtype: int64


#Deleting Unnnecessary Columns
df=data.drop(['url','phone'],axis=1) #Dropping the column like "phone" and "url" and saving the new dataset as "df"


display(df.head())


# Checking for Duplicate Records

df.duplicated().sum()

43


# Droping Duplicates

df.drop_duplicates(inplace=True)


# Checking for Duplicate Records
df.duplicated().sum()

0


display(df.duplicated().sum())

0


display(df)


#Removing all the NaN values records from the dataset

df.dropna(how='any',inplace=True)
display (df.isnull().sum())

address                        0
name                           0
online_order                   0
book_table                     0
rate                           0
votes                          0
location                       0
rest_type                      0
dish_liked                     0
cuisines                       0
approx_cost(for two people)    0
reviews_list                   0
menu_item                      0
listed_in(type)                0
listed_in(city)                0
dtype: int64


display(df)


# Displaying Column Names

display(df.columns)

Index(['address', 'name', 'online_order', 'book_table', 'rate', 'votes',
       'location', 'rest_type', 'dish_liked', 'cuisines',
       'approx_cost(for two people)', 'reviews_list', 'menu_item',
       'listed_in(type)', 'listed_in(city)'],
      dtype='object')


# Rename Column Names

df = df.rename(columns={'approx_cost(for two people)':'cost','listed_in(type)':'type',
                                  'listed_in(city)':'city'})
display (df.columns)

Index(['address', 'name', 'online_order', 'book_table', 'rate', 'votes',
       'location', 'rest_type', 'dish_liked', 'cuisines', 'cost',
       'reviews_list', 'menu_item', 'type', 'city'],
      dtype='object')


display (df)


#Getting Unique values from Cost column

display (df['cost'].unique())

array(['800', '300', '600', '700', '550', '500', '450', '650', '400',
       '750', '200', '850', '1,200', '150', '350', '250', '1,500',
       '1,300', '1,000', '100', '900', '1,100', '1,600', '950', '230',
       '1,700', '1,400', '1,350', '2,200', '2,000', '1,800', '1,900',
       '180', '330', '2,500', '2,100', '3,000', '2,800', '3,400', '40',
       '1,250', '3,500', '4,000', '2,400', '1,450', '3,200', '6,000',
       '1,050', '4,100', '2,300', '120', '2,600', '5,000', '3,700',
       '1,650', '2,700', '4,500'], dtype=object)


# Removing  the comma separator in the number. Changing the data type from string to number 

df['cost'] = df['cost'].apply(lambda x: x.replace(',','')) #Using lambda function to replace ',' from cost
df['cost'] = df['cost'].astype(float)


display (df['cost'].unique())

array([ 800.,  300.,  600.,  700.,  550.,  500.,  450.,  650.,  400.,
        750.,  200.,  850., 1200.,  150.,  350.,  250., 1500., 1300.,
       1000.,  100.,  900., 1100., 1600.,  950.,  230., 1700., 1400.,
       1350., 2200., 2000., 1800., 1900.,  180.,  330., 2500., 2100.,
       3000., 2800., 3400.,   40., 1250., 3500., 4000., 2400., 1450.,
       3200., 6000., 1050., 4100., 2300.,  120., 2600., 5000., 3700.,
       1650., 2700., 4500.])


display (df.dtypes)

address          object
name             object
online_order     object
book_table       object
rate             object
votes             int64
location         object
rest_type        object
dish_liked       object
cuisines         object
cost            float64
reviews_list     object
menu_item        object
type             object
city             object
dtype: object


#Reading unique values from the Rate column

df['rate'].unique()

array(['4.1/5', '3.8/5', '3.7/5', '4.6/5', '4.0/5', '4.2/5', '3.9/5',
       '3.0/5', '3.6/5', '2.8/5', '4.4/5', '3.1/5', '4.3/5', '2.6/5',
       '3.3/5', '3.5/5', '3.8 /5', '3.2/5', '4.5/5', '2.5/5', '2.9/5',
       '3.4/5', '2.7/5', '4.7/5', 'NEW', '2.4/5', '2.2/5', '2.3/5',
       '4.8/5', '3.9 /5', '4.2 /5', '4.0 /5', '4.1 /5', '2.9 /5',
       '2.7 /5', '2.5 /5', '2.6 /5', '4.5 /5', '4.3 /5', '3.7 /5',
       '4.4 /5', '4.9/5', '2.1/5', '2.0/5', '1.8/5', '3.4 /5', '3.6 /5',
       '3.3 /5', '4.6 /5', '4.9 /5', '3.2 /5', '3.0 /5', '2.8 /5',
       '3.5 /5', '3.1 /5', '4.8 /5', '2.3 /5', '4.7 /5', '2.4 /5',
       '2.1 /5', '2.2 /5', '2.0 /5', '1.8 /5'], dtype=object)


# Number of records related to each unique value

display(df['rate'].value_counts().head(60))

3.9/5     1691
3.9 /5    1555
4.0/5     1475
4.0 /5    1427
4.1/5     1417
4.1 /5    1410
3.8/5     1332
3.8 /5    1151
4.2 /5    1122
4.2/5     1002
4.3 /5     909
3.7/5      875
3.7 /5     771
4.3/5      771
4.4 /5     623
4.4/5      519
3.6/5      447
3.6 /5     431
4.5 /5     404
3.5/5      260
4.5/5      244
3.5 /5     222
3.4 /5     185
3.0/5      185
4.6 /5     175
2.9/5      175
3.4/5      166
2.8 /5     163
3.1/5      163
2.8/5      158
NEW        147
3.1 /5     144
2.9 /5     138
3.2/5      137
3.3/5      127
4.6/5      125
3.2 /5     113
3.0 /5     106
2.7/5      105
3.3 /5      92
4.7 /5      86
2.7 /5      85
4.7/5       81
2.6/5       77
2.6 /5      61
4.8 /5      43
2.5 /5      32
2.4/5       30
4.9 /5      30
4.9/5       25
2.5/5       25
2.3/5       25
2.4 /5      24
4.8/5       23
2.3 /5      20
2.2/5       19
2.1 /5      13
2.1/5       11
2.0 /5       7
2.2 /5       7
Name: rate, dtype: int64


#There is one Unique value "NEW", get rid of all records (147 records) with value new in the rate column

df = df.loc[df.rate !='NEW'] 
display(df)


# Display Unique Values

display (df['rate'].unique())

array(['4.1/5', '3.8/5', '3.7/5', '4.6/5', '4.0/5', '4.2/5', '3.9/5',
       '3.0/5', '3.6/5', '2.8/5', '4.4/5', '3.1/5', '4.3/5', '2.6/5',
       '3.3/5', '3.5/5', '3.8 /5', '3.2/5', '4.5/5', '2.5/5', '2.9/5',
       '3.4/5', '2.7/5', '4.7/5', '2.4/5', '2.2/5', '2.3/5', '4.8/5',
       '3.9 /5', '4.2 /5', '4.0 /5', '4.1 /5', '2.9 /5', '2.7 /5',
       '2.5 /5', '2.6 /5', '4.5 /5', '4.3 /5', '3.7 /5', '4.4 /5',
       '4.9/5', '2.1/5', '2.0/5', '1.8/5', '3.4 /5', '3.6 /5', '3.3 /5',
       '4.6 /5', '4.9 /5', '3.2 /5', '3.0 /5', '2.8 /5', '3.5 /5',
       '3.1 /5', '4.8 /5', '2.3 /5', '4.7 /5', '2.4 /5', '2.1 /5',
       '2.2 /5', '2.0 /5', '1.8 /5'], dtype=object)


#Removing '/5' from Rates

df['rate'] = df['rate'].apply(lambda x: x.replace('/5',''))
display (df['rate'].unique())

array(['4.1', '3.8', '3.7', '4.6', '4.0', '4.2', '3.9', '3.0', '3.6',
       '2.8', '4.4', '3.1', '4.3', '2.6', '3.3', '3.5', '3.8 ', '3.2',
       '4.5', '2.5', '2.9', '3.4', '2.7', '4.7', '2.4', '2.2', '2.3',
       '4.8', '3.9 ', '4.2 ', '4.0 ', '4.1 ', '2.9 ', '2.7 ', '2.5 ',
       '2.6 ', '4.5 ', '4.3 ', '3.7 ', '4.4 ', '4.9', '2.1', '2.0', '1.8',
       '3.4 ', '3.6 ', '3.3 ', '4.6 ', '4.9 ', '3.2 ', '3.0 ', '2.8 ',
       '3.5 ', '3.1 ', '4.8 ', '2.3 ', '4.7 ', '2.4 ', '2.1 ', '2.2 ',
       '2.0 ', '1.8 '], dtype=object)


#Famours Restaurants
#Restaurants with number of outlets 

df['name'].value_counts()

Onesta                                            85
Empire Restaurant                                 67
KFC                                               59
McDonald's                                        57
Pizza Hut                                         56
                                                  ..
Chakh Le India                                     1
The Creek - The Den Bengaluru                      1
The Eatery - Four Points by Sheraton Bengaluru     1
Indigo X II P                                      1
Foody Baba                                         1
Name: name, Length: 3157, dtype: int64


#the Top 20 restaurants 

df['name'].value_counts().head(20)

Onesta                    85
Empire Restaurant         67
KFC                       59
McDonald's                57
Pizza Hut                 56
Domino's Pizza            55
Smally's Resto Cafe       54
Faasos                    54
Corner House Ice Cream    52
Burger King               51
Polar Bear                48
Smoor                     47
Mani's Dum Biryani        47
Pizza Stop                46
Mainland China            45
Chung Wah                 45
Subway                    45
eat.fit                   44
Hammered                  44
Lakeview Milkbar          44
Name: name, dtype: int64


#Creating a data frame with top 20 restaurants 

chains=df['name'].value_counts()[:20]
display(chains)

Onesta                    85
Empire Restaurant         67
KFC                       59
McDonald's                57
Pizza Hut                 56
Domino's Pizza            55
Smally's Resto Cafe       54
Faasos                    54
Corner House Ice Cream    52
Burger King               51
Polar Bear                48
Smoor                     47
Mani's Dum Biryani        47
Pizza Stop                46
Mainland China            45
Chung Wah                 45
Subway                    45
eat.fit                   44
Hammered                  44
Lakeview Milkbar          44
Name: name, dtype: int64


# Visualisations- Bar chat with Top 20 restaurants  with number of outlets

plt.figure(figsize=(20,15))
chains=df['name'].value_counts()[:20]
sns.barplot(x=chains,y=chains.index,palette='deep')
plt.title("Most famous restaurants chains in Bangaluru")
plt.xlabel("Number of outlets")
plt.show()


#Restaurants with table booking

display(df['book_table'].value_counts())

No     17191
Yes     6057
Name: book_table, dtype: int64


# Creating a Pie chart 

x=df['book_table'].value_counts()
colors = ['#800080', '#0000A0']

trace=go.Pie(labels=x.index,values=x,textinfo="value",
            marker=dict(colors=colors, 
                           line=dict(color='#001000', width=2)))
layout=go.Layout(title="Table booking",width=600,height=600)
fig=go.Figure(data=[trace],layout=layout)
py.iplot(fig, filename='pie_chart_subplots')


#Restaurants delivering Online or not

sns.countplot(df['online_order'])
fig = plt.gcf()
fig.set_size_inches(10,10)
plt.title('Whether Restaurants deliver online or Not')
plt.show()


# Ratings distribution 
# most of the ratings are within 3.5 and 4.5

plt.figure(figsize=(15,7))

sns.distplot(df['rate'],bins=20)

<AxesSubplot:xlabel='rate', ylabel='Density'>


#Count of ratings as between "1 and 2", "2 and 3", "3 and 4", and "4 and 5"
# Displaying Unique Rating

display(df['rate'].unique())

array(['4.1', '3.8', '3.7', '4.6', '4.0', '4.2', '3.9', '3.0', '3.6',
       '2.8', '4.4', '3.1', '4.3', '2.6', '3.3', '3.5', '3.8 ', '3.2',
       '4.5', '2.5', '2.9', '3.4', '2.7', '4.7', '2.4', '2.2', '2.3',
       '4.8', '3.9 ', '4.2 ', '4.0 ', '4.1 ', '2.9 ', '2.7 ', '2.5 ',
       '2.6 ', '4.5 ', '4.3 ', '3.7 ', '4.4 ', '4.9', '2.1', '2.0', '1.8',
       '3.4 ', '3.6 ', '3.3 ', '4.6 ', '4.9 ', '3.2 ', '3.0 ', '2.8 ',
       '3.5 ', '3.1 ', '4.8 ', '2.3 ', '4.7 ', '2.4 ', '2.1 ', '2.2 ',
       '2.0 ', '1.8 '], dtype=object)


# minimum rating

display (df['rate'].min())

'1.8'


# maximum rating 

display(df['rate'].max())

'4.9 '


# Converting Rating from string to float

df['rate']=df['rate'].astype(float)
display(df['rate'].unique())

array([4.1, 3.8, 3.7, 4.6, 4. , 4.2, 3.9, 3. , 3.6, 2.8, 4.4, 3.1, 4.3,
       2.6, 3.3, 3.5, 3.2, 4.5, 2.5, 2.9, 3.4, 2.7, 4.7, 2.4, 2.2, 2.3,
       4.8, 4.9, 2.1, 2. , 1.8])


# Creating Histogram
group= [1,2,3,4,5]
plt.hist (df['rate'],group,histtype = 'bar',rwidth =0.5,color = 'b')
plt.show()


# Number of Restaurants with Rating  Between 1 and 2

gr_1to2=((df['rate']>=1) & (df['rate']<2)).sum()
display (gr_1to2)

5


# Number of Restaurants with Rating  Between 2 and 3

gr_2to3=((df['rate']>=2) & (df['rate']<3)).sum()
display (gr_2to3)

1179


# Number of Restaurants with Rating  Between 3 and 4

gr_3to4=((df['rate']>=3) & (df['rate']<4)).sum()
display (gr_3to4)

10153


# Number of Restaurants with Rating  more than 4

gr_4to5=(df['rate']>=4).sum()
display (gr_4to5)

11911


# Creating a Pie Chart 

slices=[gr_1to2,gr_2to3,gr_3to4,gr_4to5]   

labels=['Rating 1 to 2','Rating 2 to 3','Rating 3 to 4','Rating >4']
colors = ['#ff3333','#c2c2d6','#6699ff']
plt.pie(slices,colors=colors, labels=labels, autopct='%1.0f%%', pctdistance=.5, labeldistance=1.2,shadow=True)
fig = plt.gcf()
plt.title("Percentage of Restaurants according to their ratings")

fig.set_size_inches(10,10)
plt.show()


#Service Type 
#Count Distinct Values by Service Type

display(df['type'].value_counts())

Delivery              10657
Dine-out               8006
Desserts               1364
Cafes                  1066
Drinks & nightlife      879
Buffet                  753
Pubs and bars           523
Name: type, dtype: int64


#Service Type - count Plot
# Here the two main service types are Delivery and Dine-out

sns.countplot(df['type']).set_xticklabels(sns.countplot(df['type']).get_xticklabels(), rotation=90, ha="right")
fig = plt.gcf()
fig.set_size_inches(20,12)
plt.title('Type of Service')
plt.show()


# Distribution of cost of food for two people 
# Display Unique Cost

two_people_cost = df['cost'].unique()
display(np.sort(two_people_cost))

array([  40.,  100.,  120.,  150.,  180.,  200.,  230.,  250.,  300.,
        330.,  350.,  400.,  450.,  500.,  550.,  600.,  650.,  700.,
        750.,  800.,  850.,  900.,  950., 1000., 1050., 1100., 1200.,
       1250., 1300., 1350., 1400., 1450., 1500., 1600., 1650., 1700.,
       1800., 1900., 2000., 2100., 2200., 2300., 2400., 2500., 2600.,
       2700., 2800., 3000., 3200., 3400., 3500., 3700., 4000., 4100.,
       4500., 5000., 6000.])


#Number of Restaurants in each group 

df.groupby('cost').size()

cost
40.0         8
100.0      120
120.0        2
150.0      371
180.0       17
200.0      869
230.0       10
250.0      697
300.0     1688
330.0        4
350.0      553
400.0     2478
450.0      719
500.0     2321
550.0      440
600.0     2168
650.0      644
700.0     1202
750.0      651
800.0     1647
850.0      122
900.0      524
950.0       47
1000.0    1218
1050.0       4
1100.0     450
1200.0     903
1250.0       8
1300.0     496
1350.0      18
1400.0     447
1450.0       5
1500.0     841
1600.0     237
1650.0       6
1700.0     229
1800.0     193
1900.0      58
2000.0     298
2100.0      60
2200.0      39
2300.0       1
2400.0      22
2500.0     143
2600.0       4
2700.0       2
2800.0      31
3000.0     159
3200.0       2
3400.0      13
3500.0      25
3700.0       1
4000.0      24
4100.0       4
4500.0       2
5000.0       1
6000.0       2
dtype: int64


# Creating a box plot
from plotly.offline import iplot
trace0=go.Box(y=df['cost'],name="accepting online orders",
              marker = dict(
        color = 'rgb(113, 10, 100)',
    ))
data=[trace0]
layout=go.Layout(title="Box plot of approximate cost",width=800,height=800,yaxis=dict(title="Price"))
fig=go.Figure(data=data,layout=layout)
py.iplot(fig)


# Dist plot of cost 

plt.figure(figsize=(8,8))
sns.distplot(df['cost'])
plt.show()


#Most LIked Food
#Extracting the favourite food from column dish_liked

import re

df.index=range(df.shape[0])
likes=[]
for i in range(df.shape[0]):
    array_split=re.split(',',df['dish_liked'][i])
    for item in array_split:
        likes.append(item)


#display the number of dishes

display ('Number of dishes', len(likes))
display(likes)

'Number of dishes'

126848

['Pasta',
 ' Lunch Buffet',
 ' Masala Papad',
 ' Paneer Lajawab',
 ' Tomato Shorba',
 ' Dum Biryani',
 ' Sweet Corn Soup',
 'Momos',
 ' Lunch Buffet',
 ' Chocolate Nirvana',
 ' Thai Green Curry',
 ' Paneer Tikka',
 ' Dum Biryani',
 ' Chicken Biryani',
 'Churros',
 ' Cannelloni',
 ' Minestrone Soup',
 ' Hot Chocolate',
 ' Pink Sauce Pasta',
 ' Salsa',
 ' Veg Supreme Pizza',
 'Masala Dosa',
 'Panipuri',
 ' Gol Gappe',
 'Onion Rings',
 ' Pasta',
 ' Kadhai Paneer',
 ' Salads',
 ' Salad',
 ' Roti',
 ' Jeera Rice',
 'Farmhouse Pizza',
 ' Chocolate Banana',
 ' Virgin Mojito',
 ' Pasta',
 ' Paneer Tikka',
 ' Lime Soda',
 ' Prawn Pizza',
 'Pizza',
 ' Mocktails',
 ' Coffee',
 ' Nachos',
 ' Salad',
 ' Pasta',
 ' Sandwiches',
 'Waffles',
 ' Pasta',
 ' Coleslaw Sandwich',
 ' Choco Waffle',
 ' Tacos',
 ' Momos',
 ' Cheese Nachos',
 'Waffles',
 ' Pasta',
 ' Crispy Chicken',
 ' Honey Chilli Chicken',
 ' Sandwich',
 ' Coffee',
 ' Crepe',
 'Mocktails',
 ' Peri Fries',
 ' Lasagne',
 ' Pizza',
 ' Chicken Bbq Wings',
 ' Virgin Mojito',
 ' Nachos',
 'Coffee',
 ' Spaghetti',
 ' Pancakes',
 ' Nachos',
 ' Pasta',
 ' Sandwich',
 ' Garlic Bread',
 'Sandwich',
 ' Omelette',
 ' Ice Tea',
 ' Virgin Mojito',
 ' Hot Chocolate',
 ' Pasta Arrabiata',
 ' Hazelnut Cappuccino',
 'Churros',
 ' Cannelloni',
 ' Minestrone Soup',
 ' Hot Chocolate',
 ' Pink Sauce Pasta',
 ' Salsa',
 ' Veg Supreme Pizza',
 'Garlic Bread',
 ' Burgers',
 ' Sandwiches',
 ' Pizza',
 ' Hot Chocolate Fudge',
 ' Waffles',
 ' Potato Wedges',
 'Momos',
 ' Mushroom Bruschettas',
 ' Jalapeno Cheeseballs',
 ' Pasta',
 ' Red Velvet Shake',
 ' Red Velvet Milkshake',
 ' Maggi',
 'Pasta',
 ' Gelato',
 ' Garlic Bread',
 ' Mojito',
 ' Nachos',
 ' Paneer Pizza',
 ' Mocktails',
 'Cheese Maggi',
 ' Peri Peri Fries',
 ' Pasta Arrabiata',
 'Burgers',
 ' Caramel Milkshake',
 ' Iced Coffee',
 ' Iced Tea',
 'Pizza',
 ' Garlic Bread',
 'Pasta',
 ' Iced Tea',
 'Paratha',
 ' Sandwich',
 ' Chilli Idli',
 ' Noodles',
 ' Sandwiches',
 ' Lime Soda',
 'Pizza',
 ' Fries',
 ' Anjeer Ice Cream',
 ' Sandwiches',
 ' Gelato',
 ' Burgers',
 ' Kesar Badam',
 'Burgers',
 ' Lassi',
 ' Chicken Grill',
 ' Naan',
 ' Momos',
 ' Chicken Burger',
 ' Biryani',
 'Paneer Tikka',
 ' Garlic Bread',
 ' Thin Crust Pizza',
 ' Falafel',
 ' Veggie Pizza',
 'Rolls',
 ' Veggie Wrap',
 ' Chocolate Fantasy',
 ' Rice Bowls',
 ' Chicken Rice',
 ' Paneer Makhanwala',
 ' Chicken Tikka Wrap',
 'Raita',
 ' Gulab Jamun',
 ' Paneer Biryani',
 ' Vegetable Biryani',
 ' Gosht Dum Biryani',
 ' Chicken Biryani',
 ' Murgh Biryani',
 'Noodles',
 ' Momos',
 ' Veg Manchow Soup',
 ' Kung Pao Chicken',
 ' Chicken Lollipop',
 ' Chop Suey',
 ' Dragon Chicken',
 'Chicken Tikka Masala',
 ' Mutton Keema Dosa',
 ' Mutton Biryani',
 ' Raan Biryani',
 ' Brain Fry',
 ' Chicken Grill',
 ' Ghee Rice',
 'Dabeli',
 ' Faluda',
 ' Masala Poori',
 ' Sandwiches',
 ' Tokri Chaat',
 ' Pav Bhaji',
 ' Dahipuri',
 'Burgers',
 ' Fries',
 ' Hot Chocolate',
 'Pane Pizza',
 ' Lava Cake',
 ' Pasta',
 ' Stuffed Garlic Bread',
 ' Chocolava Cake',
 ' Tacos',
 'Farmhouse Pizza',
 ' Chocolate Banana',
 ' Virgin Mojito',
 ' Pasta',
 ' Paneer Tikka',
 ' Lime Soda',
 ' Prawn Pizza',
 'Prompt Service',
 'Biryani',
 'Butter Chicken',
 ' Noodles',
 ' Thali',
 ' Biryani',
 'Momos',
 ' Dragon Chicken',
 ' Hakka Noodles',
 ' Chowmein',
 ' Chop Suey',
 ' Chilli Garlic Noodles',
 ' Garlic Rice',
 'Paratha',
 ' Pasta',
 'Hot Chocolate Fudge',
 ' Vanilla Ice Cream',
 ' Fudge Cake',
 ' Chocolate Mousse',
 'Prawn Biryani',
 ' Dragon Chicken',
 ' Chicken Boneless Biryani',
 ' Afghani Biryani',
 ' Mughlai Biryani',
 ' Paneer Tikka Biryani',
 ' Gongura Biryani',
 'Pasta',
 ' Momos',
 ' Rolls',
 ' Chicken Roll',
 ' Chilli Chicken',
 ' Paneer Roll',
 ' Spaghetti',
 'Salads',
 ' Sandwiches',
 ' Salad',
 ' Thai Rice',
 ' Pasta',
 ' Noodles',
 ' Pizza',
 'Sushi',
 ' Noodles',
 ' Wine',
 ' Khau Suey',
 ' Mocktails',
 ' Yakisoba',
 ' Spring Roll',
 'Pasta',
 ' Pepper Pizza',
 ' Paneer Pizza',
 ' Peppy Paneer',
 ' Nachos',
 ' Supreme Pizza',
 ' Manchow Soup',
 'Sea Food',
 ' Crab Meat',
 ' Crab Soup',
 ' Fish Curry',
 ' Chicken Cafreal',
 ' Butter Naan',
 ' Butter Chicken',
 'Salads',
 ' Panneer Butter Masala',
 ' Rabri',
 ' Gajar Ka Halwa',
 ' Manchow Soup',
 ' Aam Panna',
 ' Tomato Basil Soup',
 'Grape Juice',
 ' Chicken Grill',
 ' Chicken Tikka Roll',
 ' Shawarma Roll',
 ' Chicken Shawarama',
 ' Biryani Combo',
 ' Rolls',
 'Beer',
 ' Wedges',
 ' Pizza',
 ' Nachos',
 ' Salads',
 ' Papaya Salad',
 ' Buffalo Wings',
 'Vegetable Biryani',
 ' Ginger Chicken',
 ' Chicken Biryani',
 ' Donne Biryani',
 ' Chicken Kebab',
 ' Egg Bhurji',
 ' Dum Biryani',
 'Rolls',
 ' Momos',
 ' Fries',
 ' Veggie Roll',
 ' Chicken Kebab Roll',
 ' Paneer Roll',
 ' Cheese Roll',
 'Benne Dosa',
 ' Mirchi Bajji',
 ' Filter Coffee',
 ' Poha',
 ' Tea',
 ' Butter Dosa',
 ' Crispy Dosa',
 'Masala Dosa',
 'Berryblast',
 ' Waffles',
 ' Cheesecake',
 ' Salted Caramel',
 'Chicken Boneless Biryani',
 ' Hyderabadi Biryani',
 ' Paneer Biryani',
 ' Tandoori Chicken',
 ' Vegetable Biryani',
 ' Chicken Kebab',
 ' Raita',
 'Biryani',
 ' Chicken Guntur',
 ' Thali',
 ' Buttermilk',
 ' Manchurian',
 'Chilli Chicken',
 'Panipuri',
 ' Chaat',
 ' Noodles',
 ' Dahi Bhalle',
 'Hot Chocolate Fudge',
 'Rooftop Ambience',
 'Coffee',
 ' Tea',
 ' Kesari Bath',
 ' Open Dosa',
 ' Vada',
 ' Idli',
 ' Bread Dosa',
 'Cup Cake',
 ' Chocolate Cake',
 'Masala Dosa',
 ' Tirupathi Pongal',
 ' Filter Coffee',
 ' Vada',
 ' Kharabath',
 ' Kali Dosa',
 ' Rava Idli',
 'Panner Tikka',
 ' Kulfi',
 ' Coffee',
 ' Pav Bhaji',
 ' Carrot Halwa',
 ' Fried Rice',
 ' Set Dosa',
 'Paneer Tikka',
 ' Dal Kichadi',
 ' French Fries',
 'Filter Coffee',
 ' Masala Dosa',
 ' Idli',
 ' Vada',
 'Rolls',
 ' Shawarma',
 ' Butter Chicken',
 ' Mutton Biryani',
 ' Lahori Chicken',
 ' Chicken Roll',
 ' Grape Juice',
 'Rolls',
 ' Paratha',
 ' Chicken Biryani',
 'Paratha',
 ' Mughlai Chicken',
 ' Masala Lemonade',
 ' Butter Chicken',
 ' Rajma',
 ' Sandwich',
 ' Salad',
 'Burgers',
 ' French Fries',
 'Rolls',
 ' Ice Cream Roll',
 ' Paan Ice Cream',
 ' Coffee',
 ' Vanilla Ice Cream',
 ' Chocolate Icecreams',
 ' Mississippi Mudpie',
 'Kadhai Paneer',
 ' Ghee Rice',
 ' Chicken Grill',
 ' Shawarma',
 ' Paratha',
 ' Biryani',
 ' Butter Chicken',
 'Chicken Biryani',
 ' Rolls',
 'Roast Pork',
 ' Tai Pai Chicken',
 ' Beer',
 ' Sweet Corn Chicken Soup',
 ' Masala Peanuts',
 ' Chinese Chop Suey',
 ' Chicken Lollipop',
 'Pizza',
 ' Nachos',
 ' Garlic Maggi',
 ' Burgers',
 ' Cheese Maggi',
 ' Bruschetta Bites',
 ' Sandwich',
 'Noodles',
 ' Laksa Soup',
 ' Coffee',
 ' Veg Fritters',
 ' Vegetarian',
 ' Babycorn Manchurian',
 ' Mango Salad',
 'Lassi',
 'Coffee',
 ' Chaat',
 'Pizza',
 ' Nachos',
 ' Salads',
 ' Peri Peri Fries',
 ' Brownie',
 ' Pasta Alfredo',
 ' Spaghetti',
 'Chicken Grill',
 ' Biryani',
 ' Roti',
 ' Chicken Masala',
 'Chaat',
 ' Pav Bhaji',
 ' Dabeli',
 ' Vada Pav',
 ' Tawa Pulav',
 ' Panipuri',
 ' Malpua',
 'Pizza',
 ' Burgers',
 'Burgers',
 ' Panneer Butter Masala',
 ' Pasta',
 ' Hot Dog',
 ' Fries',
 ' Cheesy Pizza',
 ' Potato Wedges',
 'Sandwiches',
 ' Thick Shakes',
 ' Paan Shake',
 ' Brownie Bomb',
 ' Fries',
 ' Spinach Corn Sandwich',
 ' Paneer Peri Peri',
 'Pizza',
 ' Pasta',
 ' Spinach Salsa',
 ' Garlic Bread',
 ' Lasagne',
 ' Bruschettas',
 ' Chocolate Bomb',
 'Friendly Staff',
 'Biryani',
 'Paratha',
 ' Vada Pav',
 ' Chaat',
 ' Pav Bhaji',
 ' Panipuri',
 ' Masala Poori',
 ' Dhokla',
 'Whole Meat Shawarma',
 ' Fries',
 'Panneer Butter Masala',
 ' Lassi',
 ' Paratha',
 ' Jeera Rice',
 ' Paneer Parantha',
 ' Bhindi Fry',
 ' Veg Kolhapuri',
 'Waffles',
 ' Pasta',
 ' Crispy Chicken',
 ' Honey Chilli Chicken',
 ' Sandwich',
 ' Coffee',
 ' Crepe',
 'Masala Papad',
 ' Paneer Satay',
 ' Vegetable Biryani',
 ' Panneer Butter Masala',
 ' Tomato Soup',
 ' Manchow Soup',
 ' Roti',
 'Shawarma',
 ' Biryani',
 ' Bbq Chicken',
 ' Chicken Grill',
 ' Raita',
 'Jowar Roti',
 ' Buttermilk',
 ' Sabudana Vada',
 ' Thali',
 ' Mirchi Bajji',
 ' Brinjal Curry',
 'Burgers',
 ' Caramel Milkshake',
 ' Iced Coffee',
 ' Iced Tea',
 'Supreme Pizza',
 ' Potato Wedges',
 ' Masala Lemonade',
 ' Tandoori Paneer',
 ' Exotic Garlic Bread',
 'Neer Dosa',
 ' Sea Food',
 ' Ghee Rice',
 ' Marwai Sukka',
 ' Egg Biryani',
 ' Kori Roti',
 ' Thai Fish',
 'White Pasta',
 ' Noodles',
 ' Babycorn Manchurian',
 ' Pizza',
 ' Veg Momos',
 ' Fry Momos',
 ' Tawa Pulav',
 'Masala Dosa',
 ' Sandwiches',
 ' Coffee',
 ' Onion Dosa',
 ' Rava Dosa',
 ' Chaat',
 ' Sandwich',
 'French Fries',
 ' Burgers',
 'Paratha',
 ' Boondi Raita',
 ' Roti',
 ' Kulfi',
 ' Panneer Butter Masala',
 ' Sweet Lassi',
 ' Dal Tadka',
 'Veg Momos',
 ' Brownie Chocolate',
 ' Palak Chaat',
 ' Mutton Biryani',
 ' Dahipuri',
 ' Fish',
 ' Sea Food',
 'Noodles',
 ' Pasta',
 ' Schezwan Momos',
 ' Chicken Momo',
 ' Teriyaki Chicken',
 ' Thukpa',
 ' Fries',
 'Spring Roll',
 ' Manchow Soup',
 ' Babycorn Manchurian',
 ' Mint Cooler',
 ' Roti',
 ' Panneer Butter Masala',
 'Biryani',
 'Pho',
 ' Papaya Salad',
 ' Coffee',
 ' Veg Momos',
 ' Rolls',
 'Waffles',
 ' Pasta',
 ' Coleslaw Sandwich',
 ' Choco Waffle',
 ' Tacos',
 ' Momos',
 ' Cheese Nachos',
 'Pizza',
 ' Pasta',
 ' Salads',
 ' Calamari',
 ' Ravioli',
 ' Pumpkin Soup',
 ' Wine',
 'Nachos',
 ' Cup Cake',
 ' Hot Chocolate Fudge',
 ' Salsa',
 ' Cheesy Garlic Bread',
 ' Waffles',
 ' Tiramisu',
 'Lime Soda',
 ' Manchow Soup',
 ' Biryani',
 ' Lemon Chicken',
 ' Tomato Soup',
 ' Butter Naan',
 ' Chilli Chicken',
 'Murgh Ghee Roast',
 ' Fish Fry',
 ' Ghee Roast Paneer',
 ' Neer Dosa',
 ' Appam',
 ' Sea Food',
 ' Chicken Sukha',
 'Ragi Mudde',
 ' Lemon Chicken',
 ' Chilli Chicken',
 ' Chicken Curry',
 ' Mutton Chops',
 ' Mutton Biryani',
 'Pasta',
 ' Potato Fries',
 ' Potato Wedges',
 ' Hot Chocolate',
 ' Chaat',
 ' Filter Coffee',
 ' Americano',
 'Sweet Lassi',
 ' Gulab Jamun',
 ' Palak Paneer Paratha',
 ' Paneer Parantha',
 'Pizza',
 ' White Sauce Pasta',
 ' Masala Garlic Bread',
 'Burgers',
 ' Fries',
 ' Mojito',
 ' Chicken Burger',
 'Paratha',
 ' Sandwich',
 ' Chilli Idli',
 ' Noodles',
 ' Sandwiches',
 ' Lime Soda',
 'Chicken Biryani',
 ' Mutton Biryani',
 ' Egg Biryani',
 ' Chicken Fry',
 ' Mutton Fry',
 'Chaat',
 ' Brownie',
 ' Onion Rings',
 ' Tomato Soup',
 ' Paneer Manchurian',
 ' Thali',
 ' Faluda',
 'Sandwiches',
 ' Chocolate Sandwich',
 ' Chaat',
 ' Veg Cheese Sandwich',
 ' Chilli Cheese',
 ' Corn Sandwich',
 'Prawn Biryani',
 ' Andhra Meal',
 ' Veg Thali',
 ' Fry Piece Biryani',
 ' Gongura Chicken',
 ' Paratha',
 'Veggie Delight',
 ' Paneer Tikka',
 ' Salad',
 'Faluda',
 ' Brownie',
 ' Sandwich',
 ' Kulfi',
 ' Fruit Salad',
 ' Sweet Lassi',
 ' Chocolate Fudge',
 'Pav Bhaji',
 ' Masala Dosa',
 ' Idli Vada',
 ' Filter Coffee',
 ' Pongal',
 ' Curd Rice',
 ' Kesari Bath',
 'Roti',
 ' Masala Dosa',
 'Onion Rings',
 ' Pasta',
 ' Kadhai Paneer',
 ' Salads',
 ' Salad',
 ' Roti',
 ' Jeera Rice',
 'Masala Papad',
 ' Paneer Satay',
 ' Paneer Hyderabadi',
 ' Butter Naan',
 ' Roti',
 'Stuffed Mushroom',
 ' Chicken Biryani',
 ' Jal-jeera',
 'Filter Coffee',
 ' Tea',
 ' Hot Coffee',
 ' Hatti Samosa',
 'Masala Dosa',
 'Badam Milk',
 ' Potato Twister',
 ' Pav Bhaji',
 ' Panipuri',
 ' Tikki Chaat',
 ' Cheese Cutlet',
 'Pasta',
 ' Iced Tea',
 'Roti',
 ' Butter Kulcha',
 ' Fried Rice',
 ' Tomato Soup',
 ' Curd Rice',
 ' Lunch Buffet',
 ' Paneer Tikka',
 'Chicken Biryani',
 'Chicken Biryani',
 ' Ragi Mudde',
 ' Mutton Chops',
 'Masala Dosa',
 ' Coffee',
 'Cocktails',
 ' Paneer Tikka',
 ' Rasmalai',
 ' Rabri',
 ' Masala Papad',
 ' Vegetable Biryani',
 ' Chicken Curry',
 'Garlic Bread',
 ' Burgers',
 ' Sandwiches',
 ' Pizza',
 ' Hot Chocolate Fudge',
 ' Waffles',
 ' Potato Wedges',
 'Veggie Pizza',
 ' Garlic Bread',
 ' Lava Cake',
 ' Chocolava',
 ' Spicy Paneer',
 'Kharabath',
 ' Crispy Vada',
 ' Filter Coffee',
 ' Idli Vada',
 ' Idli Chutney',
 ' Kesari Bath',
 ' Gulab Jamun',
 'Noodles',
 ' Veg Momos',
 ' Chowmein',
 ' Schezwan Fried Rice',
 ' Clear Soup',
 ' Sweet Corn Soup',
 ' Chicken Wings',
 'Veg Thali',
 ' Matka Kulfi',
 ' Pongal',
 ' Buttermilk',
 ' Vada',
 ' Ragi Mudde',
 ' Curd Rice',
 'Coffee',
 ' Sandwich',
 ' Hot Chocolate',
 ' Ice Tea',
 ' Sandwiches',
 ' Chicken Lasagne',
 ' Pizza',
 'Jowar Roti',
 ' Buttermilk',
 ' Jolada Rotti',
 ' Brinjal Curry',
 ' Coffee',
 ' Pongal',
 ' Vegetable Biryani',
 'Pizza',
 ' Garlic Bread',
 'Sweet Pongal',
 ' Mini Idlis',
 ' Filter Coffee',
 ' Open Masala Dosa',
 ' Pakoda',
 ' Vada',
 ' Raagi Dosa',
 'Paratha',
 ' Coffee',
 ' Halwa',
 ' Lassi',
 'Vegetable Biryani',
 ' Pineapple Rasam',
 ' Cheese Garlic Naan',
 ' Potato Cheese Ball',
 ' Bamboo Chicken Biryani',
 ' Chicken Skewer',
 ' Fish',
 'Malai Kofta',
 ' Sandwich',
 ' Naan',
 'Momos',
 ' Lunch Buffet',
 ' Chocolate Nirvana',
 ' Thai Green Curry',
 ' Paneer Tikka',
 ' Dum Biryani',
 ' Chicken Biryani',
 'Coffee',
 ' Masala Dosa',
 'Sea Food',
 ' Neer Dosa',
 ' Anjal Masala Fry',
 ' Coconut Pudding',
 ' Chicken Biryani',
 ' Roti',
 ' Fish Curry',
 'Tandoori Chicken',
 ' Rolls',
 'Pasta',
 ' Nachos',
 ' Jalapeno Poppers',
 ' Burrito Bowl',
 ' Ferrero Rocher Shake',
 ' Falafal Sandwich',
 ' Banana Waffles',
 'Pizza',
 ' Mocktails',
 ' Coffee',
 ' Nachos',
 ' Salad',
 ' Pasta',
 ' Sandwiches',
 'Aloo Paratha',
 'Rava Dosa',
 'Momos',
 ' Mushroom Bruschettas',
 ' Jalapeno Cheeseballs',
 ' Pasta',
 ' Red Velvet Shake',
 ' Red Velvet Milkshake',
 ' Maggi',
 'Pesto Pizza',
 'Sandwiches',
 ' Noodles',
 ' Sandwich',
 'Lasagne',
 ' Steak',
 ' Masala Quesadilla',
 ' Garlic Bread',
 'Breakfast Buffet',
 ' Kulcha',
 ' Almond Soup',
 ' Lunch Buffet',
 ' Roti',
 ' Mocktails',
 ' Vegetarian',
 'Hara Bhara Kabab',
 ' Crispy Corn',
 ' Patiala Paneer',
 ' Panneer Butter Masala',
 ' Dum Biryani',
 ' Jal-jeera',
 ' Butter Naan',
 'Waffles',
 ' Cup Cake',
 ' Chocolate Cake',
 'Waffles',
 'Momos',
 ' Burgers',
 ' Rolls',
 ' Sandwich',
 ' Hot Chocolate',
 ' Tea',
 ' Naga Thali',
 'Freak Shake',
 ' Fries',
 ' Pasta Alfredo',
 'Butter Chicken',
 ' Chicken Grill',
 ' Tandoori Chicken',
 ' Noodles',
 ' Biryani',
 ' Fried Rice Chicken',
 'Pizza',
 ' Pasta',
 ' Nachos',
 ' Burgers',
 ' Sandwich',
 ' Tea',
 ' Cheese Balls',
 'Masala Chai',
 ' Coffee',
 ' Masala Tea',
 ' Maggi',
 'Pasta',
 ' Gelato',
 ' Garlic Bread',
 ' Mojito',
 ' Nachos',
 ' Paneer Pizza',
 ' Mocktails',
 'Salad',
 ' Mocktails',
 ' Chocolava',
 ' Pesto Pasta',
 ' Momos',
 ' Jasmine Rice',
 ' Tom Yum Soup',
 'Vada',
 ' Masala Dosa',
 ' Idli',
 'Coffee',
 ' Masala Dosa',
 ' Vegetable Biryani',
 'Pasta',
 ' Pizza',
 ' Burgers',
 ' Pancakes',
 ' Garlic Bread',
 'Maggi',
 ' Pasta',
 ' Burgers',
 ' Sandwiches',
 ' Fries',
 'Sea Food',
 ' Neer Dosa',
 'Pizza',
 'Brownie',
 ' Mojito',
 ' Prawn Ghee Roast',
 ' Corn Tikki',
 ' Mushroom Ghee Roast',
 ' Pinacolada',
 ' Biryani',
 'Biryani',
 ' Thick Shakes',
 ' Fries',
 'Vegetarian',
 'Sweet Pongal',
 ' Curd Rice',
 'Pesto Pizza',
 ' Buffalo Wings',
 ' Cocktails',
 ' Beer',
 ' Egg Ghee Roast',
 ' Brownie',
 ' Pepper Chicken',
 'Waffles',
 ' Sandwiches',
 ' Maggi',
 ' Thick Shakes',
 ' Chocolate Sandwich',
 'Honey Chilli Potatoes',
 ' Nachos',
 ' Lemonade',
 ' Hakka Noodles',
 ' Babycorn Manchurian',
 ' Mushroom Manchurian',
 ' Spring Roll',
 'Manchow Soup',
 ' Momos',
 ' Wonton Soup',
 ' Chowmein',
 ' American Chopsuey',
 ' Chop Suey',
 ' Manchurian',
 'Pasta',
 ' Pizza',
 ' Nachos',
 ' Gelato',
 ' Ravioli',
 ' Jalapeno Poppers',
 ' Cocktails',
 'Masala Vada Pav',
 ' Cheese Vada Pav',
 ' Fries',
 'Corn Augratin',
 ' Lasagne',
 ' Fries',
 ' Garlic Bread',
 ' Pili Pili Pizza',
 ' Mexican Rice',
 ' Ravioli',
 'Salads',
 ' Sandwiches',
 ' Pasta',
 ' Mocktails',
 ' Corn Sandwich',
 ' Garden Salad',
 ' Miso Salad',
 'Coffee',
 ' Thali',
 ' Kharabath',
 ' Masala Dosa',
 ' Idli Vada',
 ' Vanilla Ice Cream',
 ' Bisi Bele Bhaath',
 'Veg Jaipuri',
 ' Neer Dosa',
 ' Kulcha',
 ' Pepper Chicken',
 ' Chilli Mushroom',
 ' Murgh Platter',
 ' Paneer Tikka',
 'Pizza',
 ' Pasta Alfredo',
 ' Brownie',
 ' Garlic Bread',
 ' Sandwiches',
 ' Lemon Mojito',
 ' Mocha Fudge',
 'Mocktails',
 ' Tomato Soup',
 ' Pasta',
 ...]


#Index

df.index=range(df.shape[0])
df.index

RangeIndex(start=0, stop=23248, step=1)


# Display top 30 dishes

print("Count of Most liked dishes")
favourite_food = pd.Series(likes).value_counts()
display(favourite_food.head(30))

Count of Most liked dishes

 Pasta              2692
 Pizza              1915
 Cocktails          1880
 Burgers            1736
 Mocktails          1623
 Biryani            1307
 Sandwiches         1287
Burgers             1256
 Coffee             1184
 Nachos             1170
 Fish               1116
 Paratha            1107
 Salads             1055
 Chicken Biryani    1004
Cocktails            891
 Fries               876
 Noodles             854
 Beer                835
 Mutton Biryani      832
 Tea                 819
Coffee               801
 Sandwich            788
 Butter Chicken      782
 Thali               770
Biryani              749
Pizza                747
 Roti                729
 Brownie             726
 Salad               677
 Hot Chocolate       672
dtype: int64


#creating the bar graph with top 20 dishes
#We can infer from the analysis that the 5 most liked dishes are Pasta,Pizza,Cocktails,Burgers,and Mocktails

ax = favourite_food.nlargest(n=20, keep='first').plot(kind='bar',figsize=(20,15),title = 'Top 30 Favourite Food counts ')

for i in ax.patches:
    ax.annotate(str(i.get_height()), (i.get_x() * 1.005, i.get_height() * 1.005))


# Restaurant Type and Count 

df['rest_type'].value_counts().head(50)

Casual Dining                    7326
Quick Bites                      5250
Cafe                             2375
Dessert Parlor                   1083
Casual Dining, Bar                985
Delivery                          704
Bar                               358
Takeaway, Delivery                342
Bar, Casual Dining                336
Pub                               324
Fine Dining                       311
Casual Dining, Cafe               304
Lounge                            298
Pub, Casual Dining                224
Food Court                        220
Bakery                            166
Cafe, Casual Dining               159
Dessert Parlor, Cafe              143
Beverage Shop, Quick Bites        137
Beverage Shop                     137
Cafe, Bakery                      128
Casual Dining, Pub                124
Microbrewery, Casual Dining       121
Cafe, Dessert Parlor              111
Sweet Shop                        105
Bakery, Quick Bites                96
Sweet Shop, Quick Bites            88
Pub, Microbrewery                  76
Dessert Parlor, Quick Bites        71
Bakery, Cafe                       69
Bakery, Dessert Parlor             62
Quick Bites, Dessert Parlor        55
Dessert Parlor, Beverage Shop      49
Mess                               49
Casual Dining, Microbrewery        47
Beverage Shop, Dessert Parlor      44
Kiosk                              44
Microbrewery, Pub                  42
Quick Bites, Sweet Shop            41
Pub, Cafe                          40
Fine Dining, Bar                   40
Cafe, Quick Bites                  40
Club                               37
Lounge, Casual Dining              37
Lounge, Bar                        36
Quick Bites, Bakery                35
Food Court, Quick Bites            34
Quick Bites, Beverage Shop         31
Pub, Bar                           28
Microbrewery                       28
Name: rest_type, dtype: int64


# Create Graph with top 20 restaurant type 
#Casual Dining, Quick Bites and Cafe are the 3 most common types of Restaurants

plt.figure(figsize=(15,7))
rest=df['rest_type'].value_counts()[:20]
sns.barplot(rest,rest.index)
plt.title("Restaurant types")
plt.xlabel("count")
plt.show()


#Famous rstaurants

plt.figure(figsize=(15,7))
chains=df['name'].value_counts()[:20]
sns.barplot(x=chains,y=chains.index,palette='Set1')
plt.title("Most famous restaurant chains",size=20,pad=20)
plt.xlabel("Number of outlets",size=15)
plt.show()


# displaying top 5 records 

display(df.head())


#Converting the online categorical variables into a numeric format

df.online_order[df.online_order == 'Yes'] = 1 
df.online_order[df.online_order == 'No'] = 0


#Displaying number of records in each catogory

df.online_order.value_counts()

1    16378
0     6870
Name: online_order, dtype: int64


#Change the data type to Number 

df.online_order = pd.to_numeric(df.online_order)
display (df.head(20))


#Update column Booke_table to 1 and 0 
#change the string categorical into to a categorical int

df.book_table[df.book_table == 'Yes'] = 1 
df.book_table[df.book_table == 'No'] = 0


#Change the data type

df.book_table = pd.to_numeric(df.book_table)


#Displaying number of records in each catogory

display(df.book_table.value_counts())

0    17191
1     6057
Name: book_table, dtype: int64


#Label encoding for required column 
#Label encode the categorical variables to make it easier to build algorithm
from sklearn.preprocessing import LabelEncoder

le = LabelEncoder()
df.location = le.fit_transform(df.location)
df.rest_type = le.fit_transform(df.rest_type)
df.cuisines = le.fit_transform(df.cuisines)
df.menu_item = le.fit_transform(df.menu_item)


display (df.head(20))


#Getting required columns to create a model

my_data=df.iloc[:,[2,3,4,5,6,7,9,10,12]]
my_data.to_csv('Zomato_df.csv')
display (my_data)


#Creating independent Variable 

x = df.iloc[:,[2,3,5,6,7,9,10,12]]
x.head()


#Create Dependent Variable 

y = df['rate']
display(y)

0        4.1
1        4.1
2        3.8
3        3.7
4        3.8
        ... 
23243    3.8
23244    3.9
23245    2.8
23246    2.5
23247    4.3
Name: rate, Length: 23248, dtype: float64


# Train Test Split

from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=.3,random_state=10)


# Creating Linear Regression Model

from sklearn.linear_model import LinearRegression
lr_model=LinearRegression()
lr_model.fit(x_train,y_train)

LinearRegression()


# Prediction and Display Accuracy 

from sklearn.metrics import r2_score
y_pred=lr_model.predict(x_test)
display (r2_score(y_test,y_pred))

0.22818828522967127


# Random Forest Regressor

from sklearn.ensemble import RandomForestRegressor
RF_Model=RandomForestRegressor(n_estimators=650,random_state=245,min_samples_leaf=.0001)
RF_Model.fit(x_train,y_train)
y_predict=RF_Model.predict(x_test)
display (r2_score(y_test,y_predict))

0.8809706960047533


#Preparing Extra Tree Regression

from sklearn.ensemble import  ExtraTreesRegressor
ET_Model=ExtraTreesRegressor(n_estimators = 120)
ET_Model.fit(x_train,y_train)
y_predict=ET_Model.predict(x_test)


from sklearn.metrics import r2_score
display(r2_score(y_test,y_predict))

0.9326250187028753


#Creating pickle file to use it later 
# Saving model to disk

import pickle 
pickle.dump(ET_Model, open('model.pkl','wb'))
model=pickle.load(open('model.pkl','rb'))

	address	name	online_order	book_table	rate	votes	location	rest_type	dish_liked	cuisines	cost	reviews_list	menu_item	type	city
0	942, 21st Main Road, 2nd Stage, Banashankari, ...	Jalsa	1	1	4.1	775	1	20	Pasta, Lunch Buffet, Masala Papad, Paneer Laja...	1386	800.0	[('Rated 4.0', 'RATED\n A beautiful place to ...	5047	Buffet	Banashankari
1	2nd Floor, 80 Feet Road, Near Big Bazaar, 6th ...	Spice Elephant	1	0	4.1	787	1	20	Momos, Lunch Buffet, Chocolate Nirvana, Thai G...	594	800.0	[('Rated 4.0', 'RATED\n Had been here for din...	5047	Buffet	Banashankari
2	1112, Next to KIMS Medical College, 17th Cross...	San Churro Cafe	1	0	3.8	918	1	16	Churros, Cannelloni, Minestrone Soup, Hot Choc...	484	800.0	[('Rated 3.0', "RATED\n Ambience is not that ...	5047	Buffet	Banashankari
3	1st Floor, Annakuteera, 3rd Stage, Banashankar...	Addhuri Udupi Bhojana	0	0	3.7	88	1	62	Masala Dosa	1587	300.0	[('Rated 4.0', "RATED\n Great food and proper...	5047	Buffet	Banashankari
4	10, 3rd Floor, Lakshmi Associates, Gandhi Baza...	Grand Village	0	0	3.8	166	4	20	Panipuri, Gol Gappe	1406	600.0	[('Rated 4.0', 'RATED\n Very good restaurant ...	5047	Buffet	Banashankari
5	37, 5-1, 4th Floor, Bosco Court, Gandhi Bazaar...	Timepass Dinner	1	0	3.8	286	4	20	Onion Rings, Pasta, Kadhai Paneer, Salads, Sal...	1156	600.0	[('Rated 3.0', 'RATED\n Food 3/5\nAmbience 3/...	5047	Buffet	Banashankari
6	2469, 3rd Floor, 24th Cross, Opposite BDA Comp...	Onesta	1	1	4.6	2556	1	22	Farmhouse Pizza, Chocolate Banana, Virgin Moji...	1468	600.0	[('Rated 5.0', 'RATED\n I personally really l...	5047	Cafes	Banashankari
7	1, 30th Main Road, 3rd Stage, Banashankari, Ba...	Penthouse Cafe	1	0	4.0	324	1	13	Pizza, Mocktails, Coffee, Nachos, Salad, Pasta...	462	700.0	[('Rated 3.0', "RATED\n I had been to this pl...	5047	Cafes	Banashankari
8	2470, 21 Main Road, 25th Cross, Banashankari, ...	Smacznego	1	0	4.2	504	1	13	Waffles, Pasta, Coleslaw Sandwich, Choco Waffl...	485	550.0	[('Rated 4.0', "RATED\n Easy to locate\nVFM 3...	5047	Cafes	Banashankari
9	12,29 Near PES University Back Gate, D'Souza N...	CafÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂ© Down The A...	1	0	4.1	402	1	13	Waffles, Pasta, Crispy Chicken, Honey Chilli C...	332	500.0	[('Rated 4.0', 'RATED\n We ended up here on a...	5047	Cafes	Banashankari
10	941, 3rd FLOOR, 21st Main, 22nd Cross, Banasha...	Cafe Shuffle	1	1	4.2	150	1	13	Mocktails, Peri Fries, Lasagne, Pizza, Chicken...	462	600.0	[('Rated 1.0', "RATED\n \n\nHorrible. Not even...	5047	Cafes	Banashankari
11	6th Block, 3rd Stage, Banashankari, Bangalore	The Coffee Shack	1	1	4.2	164	1	13	Coffee, Spaghetti, Pancakes, Nachos, Pasta, Sa...	380	500.0	[('Rated 4.0', "RATED\n Food - 4/5\nAmbience ...	5047	Cafes	Banashankari
12	111, Sapphire Toys Building, 100 Feet Ring Roa...	Caf-Eleven	0	0	4.0	424	1	13	Sandwich, Omelette, Ice Tea, Virgin Mojito, Ho...	383	450.0	[('Rated 2.0', "RATED\n This is a hookah cafe...	5047	Cafes	Banashankari
13	1112, Next to KIMS Medical College, 17th Cross...	San Churro Cafe	1	0	3.8	918	1	16	Churros, Cannelloni, Minestrone Soup, Hot Choc...	484	800.0	[('Rated 3.0', "RATED\n Ambience is not that ...	5047	Cafes	Banashankari
14	2303, 21st Cross, K R Road, 2nd Stage, Banasha...	Cafe Vivacity	1	0	3.8	90	1	13	Garlic Bread, Burgers, Sandwiches, Pizza, Hot ...	332	650.0	[('Rated 2.0', 'RATED\n Not so good place as ...	5047	Cafes	Banashankari
15	241, 4th Floor, 100 Feet Ring Road, Opposite K...	Catch-up-ino	1	0	3.9	133	1	13	Momos, Mushroom Bruschettas, Jalapeno Cheeseba...	441	800.0	[('Rated 1.0', "RATED\n This place is right o...	5047	Cafes	Banashankari
16	405, 24th Cross, 9th Main, 2nd Stage, Banashan...	Kirthi's Biryani	1	0	3.8	144	1	13	Pasta, Gelato, Garlic Bread, Mojito, Nachos, P...	533	700.0	[('Rated 3.0', "RATED\n New place.. though it...	5047	Cafes	Banashankari
17	504, CJ Venkata Das Road, Padmanabhangar, 2nd ...	T3H Cafe	0	0	3.9	93	1	13	Cheese Maggi, Peri Peri Fries, Pasta Arrabiata	456	300.0	[('Rated 4.0', "RATED\n Happy to see such a c...	5047	Cafes	Banashankari
18	146, 50 ft Road, 3rd Phase, 3rd Stage, Near Ka...	The Vintage Cafe	1	0	3.0	62	1	13	Burgers, Caramel Milkshake, Iced Coffee, Iced Tea	444	400.0	[('Rated 2.0', "RATED\n This is a tiny cafÃ\...	5047	Cafes	Banashankari
19	3353, 2nd Cross, Opposite Auden School, Hosker...	Woodee Pizza	1	0	3.7	180	1	13	Pizza, Garlic Bread	496	500.0	[('Rated 3.0', 'RATED\n Decided to take a bre...	5047	Cafes	Banashankari

Importing Libraries¶

Exploratoery Data Analysis (EDA)¶

Visualization¶

Model Creation¶

	url	address	name	online_order	book_table	rate	votes	phone	location	rest_type	dish_liked	cuisines	approx_cost(for two people)	reviews_list	menu_item	listed_in(type)	listed_in(city)
0	https://www.zomato.com/bangalore/jalsa-banasha...	942, 21st Main Road, 2nd Stage, Banashankari, ...	Jalsa	Yes	Yes	4.1/5	775	080 42297555\r\n+91 9743772233	Banashankari	Casual Dining	Pasta, Lunch Buffet, Masala Papad, Paneer Laja...	North Indian, Mughlai, Chinese	800	[('Rated 4.0', 'RATED\n A beautiful place to ...	[]	Buffet	Banashankari
1	https://www.zomato.com/bangalore/spice-elephan...	2nd Floor, 80 Feet Road, Near Big Bazaar, 6th ...	Spice Elephant	Yes	No	4.1/5	787	080 41714161	Banashankari	Casual Dining	Momos, Lunch Buffet, Chocolate Nirvana, Thai G...	Chinese, North Indian, Thai	800	[('Rated 4.0', 'RATED\n Had been here for din...	[]	Buffet	Banashankari
2	https://www.zomato.com/SanchurroBangalore?cont...	1112, Next to KIMS Medical College, 17th Cross...	San Churro Cafe	Yes	No	3.8/5	918	+91 9663487993	Banashankari	Cafe, Casual Dining	Churros, Cannelloni, Minestrone Soup, Hot Choc...	Cafe, Mexican, Italian	800	[('Rated 3.0', "RATED\n Ambience is not that ...	[]	Buffet	Banashankari
3	https://www.zomato.com/bangalore/addhuri-udupi...	1st Floor, Annakuteera, 3rd Stage, Banashankar...	Addhuri Udupi Bhojana	No	No	3.7/5	88	+91 9620009302	Banashankari	Quick Bites	Masala Dosa	South Indian, North Indian	300	[('Rated 4.0', "RATED\n Great food and proper...	[]	Buffet	Banashankari
4	https://www.zomato.com/bangalore/grand-village...	10, 3rd Floor, Lakshmi Associates, Gandhi Baza...	Grand Village	No	No	3.8/5	166	+91 8026612447\r\n+91 9901210005	Basavanagudi	Casual Dining	Panipuri, Gol Gappe	North Indian, Rajasthani	600	[('Rated 4.0', 'RATED\n Very good restaurant ...	[]	Buffet	Banashankari
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
51712	https://www.zomato.com/bangalore/best-brews-fo...	Four Points by Sheraton Bengaluru, 43/3, White...	Best Brews - Four Points by Sheraton Bengaluru...	No	No	3.6 /5	27	080 40301477	Whitefield	Bar	NaN	Continental	1,500	[('Rated 5.0', "RATED\n Food and service are ...	[]	Pubs and bars	Whitefield
51713	https://www.zomato.com/bangalore/vinod-bar-and...	Number 10, Garudachar Palya, Mahadevapura, Whi...	Vinod Bar And Restaurant	No	No	NaN	0	+91 8197675843	Whitefield	Bar	NaN	Finger Food	600	[]	[]	Pubs and bars	Whitefield
51714	https://www.zomato.com/bangalore/plunge-sherat...	Sheraton Grand Bengaluru Whitefield Hotel & Co...	Plunge - Sheraton Grand Bengaluru Whitefield H...	No	No	NaN	0	NaN	Whitefield	Bar	NaN	Finger Food	2,000	[]	[]	Pubs and bars	Whitefield
51715	https://www.zomato.com/bangalore/chime-sherato...	Sheraton Grand Bengaluru Whitefield Hotel & Co...	Chime - Sheraton Grand Bengaluru Whitefield Ho...	No	Yes	4.3 /5	236	080 49652769	ITPL Main Road, Whitefield	Bar	Cocktails, Pizza, Buttermilk	Finger Food	2,500	[('Rated 4.0', 'RATED\n Nice and friendly pla...	[]	Pubs and bars	Whitefield
51716	https://www.zomato.com/bangalore/the-nest-the-...	ITPL Main Road, KIADB Export Promotion Industr...	The Nest - The Den Bengaluru	No	No	3.4 /5	13	+91 8071117272	ITPL Main Road, Whitefield	Bar, Casual Dining	NaN	Finger Food, North Indian, Continental	1,500	[('Rated 5.0', 'RATED\n Great ambience , look...	[]	Pubs and bars	Whitefield